Software Engineer - Data Flywheel

Perplexity serves tens of millions of users daily with reliable, high-quality answers grounded in an LLM-first search engine and specialized data sources.

The Answer

Quality team ensures that our prompts, tools, search, and specialized datasets, combined with both frontier and in-house models, create the best possible experience for our users. As our product evolves, our evaluations must remain fast, accurate, and actionable. In this role, you will build the data flywheel that serves teams across Perplexity.

Responsibilities

Build the systems and pipelines that enable Search, Product, and other teams to independently access and utilize reliable eval verdicts without bottlenecks

Take ownership of the "evals-to-product" loop, autonomously determining the best way to turn raw signals into durable datasets that power decision-making across the company

Build a robust simulator pipeline capable of replaying user interactions with the product in formats legible to LLMs and VLMs, reflecting product changes as they are shipped

Maintain data trust by implementing monitoring, lineage, and quality checks, ensuring downstream consumers can rely on the results implicitly

Operate in a small, high-impact team where your work directly shapes how Perplexity measures and improves Answer Quality

Qualifications

3+ years of software engineering experience shipping production systems

Strong proficiency in Python and SQL with the ability to write production-grade, maintainable code

Experience with big data systems including distributed compute and large-scale storage

Solid fundamentals in data modeling, system design, and debugging distributed systems

Experience with AWS and lakehouse ecosystems like Databricks or Spark

Comfortable with agentic coding workflows and using AI-assisted development tools to iterate faster

Preferred Qualifications

Data engineering background including pipelines, orchestration, and warehousing patterns

Familiarity with LLM/VLM interfaces, tokenization, structured formats, and multimodal payloads

Experience with evaluation platforms, experimentation systems, or machine learning infrastructure

Prior work supporting customer-facing products at scale